


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_SCREAMING_SNAKE_CASE     :					Dict      =       logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE     :					List[Any]      =       {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE     :					str      =       {
    "allenai/led-base-16384": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							(
         list(range(ord('''!'''							)				,ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,ord('''ÿ'''							) + 1							)							)
     )
     snake_case											=							bs[:]
     snake_case											=							0
     for b in range(2**8							):
          if b not in bs:
               bs.append(UpperCamelCase_							)
               cs.append(2**8 + n							)
               n += 1
     snake_case											=							[chr(UpperCamelCase_							) for n in cs]
     return dict(zip(UpperCamelCase_				,UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							set()
     snake_case											=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          snake_case											=							char
     return pairs

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__         =						['input_ids', 'attention_mask']
    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case="replace"     ,   __snake_case="<s>"     ,   __snake_case="</s>"     ,   __snake_case="</s>"     ,   __snake_case="<s>"     ,   __snake_case="<unk>"     ,   __snake_case="<pad>"     ,   __snake_case="<mask>"     ,   __snake_case=False     ,   **__snake_case     ,   ):
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else bos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else eos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else sep_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else cls_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else unk_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else mask_token

         super().__init__(
             errors=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   unk_token=__snake_case     ,   sep_token=__snake_case     ,   cls_token=__snake_case     ,   pad_token=__snake_case     ,   mask_token=__snake_case     ,   add_prefix_space=__snake_case     ,   **__snake_case     ,   )

         with open(__snake_case     ,   encoding='''utf-8'''       ) as vocab_handle:
              snake_case											=							json.load(__snake_case       )
         snake_case											=							{v: k for k, v in self.encoder.items()}
         snake_case											=							errors  # how to handle errors in decoding
         snake_case											=							bytes_to_unicode()
         snake_case											=							{v: k for k, v in self.byte_encoder.items()}
         with open(__snake_case     ,   encoding='''utf-8'''       ) as merges_handle:
              snake_case											=							merges_handle.read().split('''\n'''       )[1:-1]
         snake_case											=							[tuple(merge.split()       ) for merge in bpe_merges]
         snake_case											=							dict(zip(__snake_case     ,   range(len(__snake_case       )       )       )       )
         snake_case											=							{}
         snake_case											=							add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case											=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
    @property
    # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
    def 						a_     (			self       ):
         return len(self.encoder       )
    def 						a_     (			self       ):
         return dict(self.encoder     ,   **self.added_tokens_encoder       )
    def 						a_     (			self     ,   __snake_case       ):
         if token in self.cache:
              return self.cache[token]
         snake_case											=							tuple(__snake_case       )
         snake_case											=							get_pairs(__snake_case       )

         if not pairs:
              return token

         while True:
              snake_case											=							min(__snake_case     ,   key=lambda __snake_case       : self.bpe_ranks.get(__snake_case     ,   float('''inf'''       )       )       )
              if bigram not in self.bpe_ranks:
                   break
              snake_case					,    snake_case											=							bigram
              snake_case											=							[]
              snake_case											=							0
              while i < len(__snake_case       ):
                   try:
                        snake_case											=							word.index(__snake_case     ,   __snake_case       )
                   except ValueError:
                        new_word.extend(word[i:]       )
                        break
                   else:
                        new_word.extend(word[i:j]       )
                        snake_case											=							j

                   if word[i] == first and i < len(__snake_case       ) - 1 and word[i + 1] == second:
                        new_word.append(first + second       )
                        i += 2
                   else:
                        new_word.append(word[i]       )
                        i += 1
              snake_case											=							tuple(__snake_case       )
              snake_case											=							new_word
              if len(__snake_case       ) == 1:
                   break
              else:
                   snake_case											=							get_pairs(__snake_case       )
         snake_case											=							''' '''.join(__snake_case       )
         snake_case											=							word
         return word
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							[]
         for token in re.findall(self.pat     ,   __snake_case       ):
              snake_case											=							''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case       ).split(''' '''       )       )
         return bpe_tokens
    def 						a_     (			self     ,   __snake_case       ):
         return self.encoder.get(__snake_case     ,   self.encoder.get(self.unk_token       )       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.decoder.get(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							''''''.join(__snake_case       )
         snake_case											=							bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     ,   errors=self.errors       )
         return text
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if not os.path.isdir(__snake_case       ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
              return
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as f:
              f.write(json.dumps(self.encoder     ,   indent=2     ,   sort_keys=__snake_case     ,   ensure_ascii=__snake_case       ) + '''\n'''       )

         snake_case											=							0
         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as writer:
              writer.write('''#version: 0.2\n'''       )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,   key=lambda __snake_case       : kv[1]       ):
                   if index != token_index:
                        logger.warning(
                            F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                            ''' Please check that the tokenizer is not corrupted!'''       )
                        snake_case											=							token_index
                   writer.write(''' '''.join(__snake_case       ) + '''\n'''       )
                   index += 1

         return vocab_file, merge_file
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case											=							[self.cls_token_id]
         snake_case											=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = False       ):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__snake_case     ,   token_ids_a=__snake_case     ,   already_has_special_tokens=__snake_case       )

         if token_ids_a is None:
              return [1] + ([0] * len(__snake_case       )) + [1]
         return [1] + ([0] * len(__snake_case       )) + [1, 1] + ([0] * len(__snake_case       )) + [1]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False     ,   **__snake_case       ):
         snake_case											=							kwargs.pop('''add_prefix_space'''     ,   self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(__snake_case       ) > 0 and not text[0].isspace()):
              snake_case											=							''' ''' + text
         return (text, kwargs)




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = PaddingStrategy.DO_NOT_PAD     ,   __snake_case = None     ,   __snake_case = None     ,   ):
         snake_case											=							super()._pad(
             encoded_inputs=__snake_case     ,   max_length=__snake_case     ,   padding_strategy=__snake_case     ,   pad_to_multiple_of=__snake_case     ,   return_attention_mask=__snake_case     ,   )

         # Load from model defaults
         if return_attention_mask is None:
              snake_case											=							'''attention_mask''' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              snake_case											=							encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              snake_case											=							len(encoded_inputs['''global_attention_mask''']       ) != len(__snake_case       )

              if needs_to_be_padded:
                   snake_case											=							len(__snake_case       ) - len(encoded_inputs['''global_attention_mask''']       )

                   if self.padding_side == "right":
                        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                        snake_case											=							(
                            encoded_inputs['''global_attention_mask'''] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        snake_case											=							[-1] * difference + encoded_inputs[
                            '''global_attention_mask'''
                        ]
                   else:
                        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

         return encoded_inputs








import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=7     ,   __snake_case=3     ,   __snake_case=3_0     ,   __snake_case=4_0_0     ,   __snake_case=True     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=1 / 2_5_5     ,   __snake_case=True     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=True     ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case											=							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							min_resolution
         snake_case											=							max_resolution
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean
         snake_case											=							image_std
         snake_case											=							do_pad
    def 						a_     (			self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if not batched:
              snake_case											=							image_inputs[0]
              if isinstance(__snake_case     ,   Image.Image       ):
                   snake_case					,    snake_case											=							image.size
              else:
                   snake_case					,    snake_case											=							image.shape[1], image.shape[2]
              if w < h:
                   snake_case											=							int(self.size['''shortest_edge'''] * h / w       )
                   snake_case											=							self.size['''shortest_edge''']
              elif w > h:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							int(self.size['''shortest_edge'''] * w / h       )
              else:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							self.size['''shortest_edge''']

         else:
              snake_case											=							[]
              for image in image_inputs:
                   snake_case					,    snake_case											=							self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[0]       )[0]
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						DetrImageProcessor if is_vision_available() else None
    def 						a_     (			self       ):
         snake_case											=							DetrImageProcessingTester(self       )
    @property
    def 						a_     (			self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_mean'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_std'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_normalize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_rescale'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''rescale_factor'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_resize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''size'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_pad'''       )       )
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )

         snake_case											=							self.image_processing_class.from_dict(
             self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   Image.Image       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   np.ndarray       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   torch.Tensor       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    @slow
    def 						a_     (			self       ):
         # prepare image and target
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




    @slow
    def 						a_     (			self       ):
         # prepare image, target and masks_path
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         snake_case											=							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify masks
         snake_case											=							8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,   __snake_case       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




